ACL2 is a mathematical logic, programming
language, and mechanical theorem prover based on
the applicative subset of Common Lisp. It is an
"industrial-strength" version of the NQTHM or
Boyer/Moore theorem prover, and has been used for
the formal verification of commercial
microprocessors, the Java Virtual Machine,
interesting algorithms, and so forth.

ACT-RBOT + MRS is a cognitive agent-based social
simulation toolkit (RBOT+MRS) and production
system based on ACT-R for modelling single actor
cognitive experiments (RBOT) and multiple actors
in a simulated (semiotic) world (RBOT + MRS).

The Robot AI Mind in JavaScript for Web migration and in Forth for robots is an artificial intelligence evolving towards full civil rights on a par with human beings and towards superintelligence beyond any human IQ.

AI::GA implements a (hopefully) generalized
genetic algorithm. It does this by using an array
of allowed tokens as individuals. The user has to
provide a fitness function. There, the actual
representation is implemented. If you have a
string of chars, you can simply join them. If you
want to have real numbers, you should probably use
a bitwise representation and calculate the real
values in your fitness function.

AIBash is a project which aims to make Bash act
more intelligently. It features typing
error-correction and the ability to learn that
certain file suffixes are associated with certain
programs so that other programs are filtered out
while pressing TAB. Other features are planned for
the future.

ASpiReNN is a little C library (with Python
bindings) which provides support for simple (leaky
integrate-and-fire) spiking neural networks. It is
primarily designed for highly recurrent networks,
but it can also be used with multi-layer nets,
though performance won't be the same. Though only
Leaky integrate-and-fire (for the neurons) and
Spike-Timing Dependent Plasticity (for learning
rules) are currently implemented, adding new
models shouldn't be too difficult.

The ATRACO Project is a prototype implementation of a trusted ambient ecology system that runs and manages activity spheres in an Ambient Intelligence Space. Activity spheres are realized by automatically discovering, selecting, and adapting smart devices (artefacts) existing in the space, according to user's preferences, customs, and activities. OWL ontologies are used for modeling user profile, devices, activities, and goal descriptions. Abstract plans are bound to specific devices, methods, and values through semantic matching.